docstr: Register this command to argparse so it's available for the transformer-cli
  
  Args:
      parser: Root parser to register command-specific arguments
docstr_tok: ["Register","this","command","to","argparse","so","it","'s","available","for","the","transformer-cli","Args",":","parser",":","Root","parser","to","register","command-specific","arguments"]
code: def register_subcommand(parser: ArgumentParser):
      """
      Register this command to argparse so it's available for the transformer-cli
  
      Args:
          parser: Root parser to register command-specific arguments
      """
      train_parser = parser.add_parser(
          "pt-to-tf",
          help=(
              "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
              " Can also be used to validate existing weights without opening PRs, with --no-pr."
          ),
      )
      train_parser.add_argument(
          "--model-name",
          type=str,
          required=True,
          help="The model name, including owner/organization, as seen on the hub.",
      )
      train_parser.add_argument(
          "--local-dir",
          type=str,
          default="",
          help="Optional local directory of the model repository. Defaults to /tmp/{model_name}",
      )
      train_parser.add_argument(
          "--max-error",
          type=float,
          default=MAX_ERROR,
          help=(
              f"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
          ),
      )
      train_parser.add_argument(
          "--new-weights",
          action="store_true",
          help="Optional flag to create new TensorFlow weights, even if they already exist.",
      )
      train_parser.add_argument(
          "--no-pr",
          action="store_true",
          help="Optional flag to NOT open a PR with converted weights.",
      )
      train_parser.add_argument(
          "--push",
          action="store_true",
          help="Optional flag to push the weights directly to `main` (requires permissions)",
      )
      train_parser.add_argument(
          "--extra-commit-description",
          type=str,
          default="",
          help="Optional additional commit description to use when opening a PR (e.g. to tag the owner).",
      )
      train_parser.add_argument(
          "--override-model-class",
          type=str,
          default=None,
          help="If you think you know better than the auto-detector, you can specify the model class here. "
          "Can be either an AutoModel class or a specific model class like BertForSequenceClassification.",
      )
      train_parser.set_defaults(func=convert_command_factory)
  
file_path: transformers/src/transformers/commands/pt_to_tf.py
imports: import inspect
  import os
  from argparse import ArgumentParser, Namespace
  from importlib import import_module
  import huggingface_hub
  import numpy
  from packaging import version
  from ..None import FEATURE_EXTRACTOR_MAPPING, IMAGE_PROCESSOR_MAPPING, PROCESSOR_MAPPING, TOKENIZER_MAPPING, AutoConfig, AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer, is_datasets_available, is_tf_available, is_torch_available
  from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
  from .None import BaseTransformersCLICommand
label_desc: Use image node_id 1 for calling the PTtoTFCommand obj's underlying member method code with example usage: obj.register_subcommand(parser) without return types